Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Cranfield University
- ;
- ; Swansea University
- ; The University of Manchester
- University of Nottingham
- University of Cambridge
- ; Cranfield University
- ; University of Birmingham
- ; University of Bristol
- ; University of Oxford
- AALTO UNIVERSITY
- University of Sheffield
- ; Brunel University London
- ; The University of Edinburgh
- ; University of Surrey
- ; City St George’s, University of London
- ; University of Cambridge
- ; University of Sheffield
- ; University of Southampton
- ; University of Sussex
- ; University of Warwick
- Abertay University
- Imperial College London
- University of Newcastle
- ; Aston University
- ; Coventry University Group
- ; Durham University
- ; Loughborough University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; University of Greenwich
- ; University of Nottingham
- ; University of Strathclyde
- ; University of York
- Aston University
- UNIVERSITY OF SOUTHAMPTON
- University of Manchester
- University of Oxford
- Utrecht University
- 29 more »
- « less
-
Field
-
will dynamically adjust turbine parameters such as yaw, pitch, and torque to maximize Annual Energy Production (AEP) while minimizing component stress. Additionally, a hybrid predictive maintenance model
-
. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
-
at the intersection of environmental planning, urban design, and digital innovation. You will be part of a dynamic team working to shape evidence-based policy and design solutions that improve air quality
-
analysis. Candidates are also expected to bring their own knowledge and approaches to design and execution of the project. The candidate will work in a dynamic team of scientists and collaborate with group
-
modelling of laser shock peening. Molecular Dynamics (MD) and Finite Element (FE) simulations will be combined to account for the complex physical phenomena and their different scales. The interdependence
-
of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
-
or in an academic role. We will help you develop into a dynamic, confident and highly competent researcher with wider transferable skills (communication, project management and leadership) with
-
research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
-
, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
-
interdisciplinary training in AI, modelling, and data analytics Contribute to real-world engineering applications Be part of the dynamic research community at the Zienkiewicz Institute for Modelling, Data and AI